{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0074676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "#!pip install keras\n",
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np # linear algebra\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import math   \n",
    "from datetime import datetime, date \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from tensorflow import keras\n",
    "from tensorflow.python.keras import layers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a2c0090-934b-4ade-aa50-a7717d83980d",
   "metadata": {},
   "source": [
    "# Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1cd40e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>행정구역</th>\n",
       "      <th>지역코드</th>\n",
       "      <th>Financial</th>\n",
       "      <th>rainfall days&gt;60</th>\n",
       "      <th>sum rainfall</th>\n",
       "      <th>average rainfall_event</th>\n",
       "      <th>maximum rainfall</th>\n",
       "      <th>duration</th>\n",
       "      <th>하천면적</th>\n",
       "      <th>불투수면 면적</th>\n",
       "      <th>...</th>\n",
       "      <th>주거지역 (㎡)</th>\n",
       "      <th>상업지역 (㎡)</th>\n",
       "      <th>공업지역 (㎡)</th>\n",
       "      <th>도로면적</th>\n",
       "      <th>유수지 면적</th>\n",
       "      <th>유수지 저수율(m3)</th>\n",
       "      <th>제방면적</th>\n",
       "      <th>녹지지역 (㎡)</th>\n",
       "      <th>검색량/Days</th>\n",
       "      <th>검색량</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>경상북도 성주군</td>\n",
       "      <td>47840</td>\n",
       "      <td>785261.0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17</td>\n",
       "      <td>20750195.1</td>\n",
       "      <td>65.7059</td>\n",
       "      <td>...</td>\n",
       "      <td>139.46</td>\n",
       "      <td>12.59</td>\n",
       "      <td>137.9</td>\n",
       "      <td>14529273.3</td>\n",
       "      <td>577681</td>\n",
       "      <td>94634</td>\n",
       "      <td>1478433.7</td>\n",
       "      <td>466.1384</td>\n",
       "      <td>17.258757</td>\n",
       "      <td>293.398873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       행정구역   지역코드  Financial  rainfall days>60  sum rainfall  \\\n",
       "0  경상북도 성주군  47840   785261.0                 0          52.0   \n",
       "\n",
       "   average rainfall_event  maximum rainfall  duration        하천면적  불투수면 면적  \\\n",
       "0               17.333333              41.0        17  20750195.1  65.7059   \n",
       "\n",
       "   ...  주거지역 (㎡)  상업지역 (㎡)  공업지역 (㎡)        도로면적  유수지 면적  유수지 저수율(m3)  \\\n",
       "0  ...    139.46     12.59     137.9  14529273.3  577681        94634   \n",
       "\n",
       "        제방면적  녹지지역 (㎡)   검색량/Days         검색량  \n",
       "0  1478433.7  466.1384  17.258757  293.398873  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"AHP_2011_2018.csv\", encoding= 'cp949')\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "052e5593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sgg</th>\n",
       "      <th>code</th>\n",
       "      <th>damage</th>\n",
       "      <th>h1</th>\n",
       "      <th>h2</th>\n",
       "      <th>h3</th>\n",
       "      <th>h4</th>\n",
       "      <th>h5</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "      <th>...</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>v4</th>\n",
       "      <th>v5</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5_day</th>\n",
       "      <th>c5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>경상북도 성주군</td>\n",
       "      <td>47840</td>\n",
       "      <td>785261.0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17</td>\n",
       "      <td>20750195.1</td>\n",
       "      <td>65.7059</td>\n",
       "      <td>...</td>\n",
       "      <td>139.46</td>\n",
       "      <td>12.59</td>\n",
       "      <td>137.9</td>\n",
       "      <td>14529273.3</td>\n",
       "      <td>577681</td>\n",
       "      <td>94634</td>\n",
       "      <td>1478433.7</td>\n",
       "      <td>466.1384</td>\n",
       "      <td>17.258757</td>\n",
       "      <td>293.398873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sgg   code    damage  h1    h2         h3    h4  h5          e1  \\\n",
       "0  경상북도 성주군  47840  785261.0   0  52.0  17.333333  41.0  17  20750195.1   \n",
       "\n",
       "        e2  ...      v2     v3     v4          v5      c1     c2         c3  \\\n",
       "0  65.7059  ...  139.46  12.59  137.9  14529273.3  577681  94634  1478433.7   \n",
       "\n",
       "         c4     c5_day          c5  \n",
       "0  466.1384  17.258757  293.398873  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['sgg', 'code', 'damage', 'h1', 'h2', 'h3', 'h4', 'h5', 'e1', 'e2', 'e3', 'e4', 'v1', 'v2','v3', 'v4', 'v5', 'c1', 'c2', 'c3', 'c4', 'c5_day', 'c5']\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06b92b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df[[ 'h1', 'h2', 'h3', 'h4', 'h5', 'e1', 'e2', 'e3', 'e4', 'v1', 'v2','v3', 'v4', 'v5', 'c1', 'c2', 'c3', 'c4']]\n",
    "y = df['damage']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81844715-deeb-41fb-8796-ed78b1edf8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1</th>\n",
       "      <th>h2</th>\n",
       "      <th>h3</th>\n",
       "      <th>h4</th>\n",
       "      <th>h5</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "      <th>e3</th>\n",
       "      <th>e4</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>v4</th>\n",
       "      <th>v5</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3558634.5</td>\n",
       "      <td>19.8763</td>\n",
       "      <td>41.822830</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6365.958035</td>\n",
       "      <td>51.04</td>\n",
       "      <td>6.49</td>\n",
       "      <td>7.98</td>\n",
       "      <td>6336136.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213930.2</td>\n",
       "      <td>16.6463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>45.454545</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61</td>\n",
       "      <td>26068595.4</td>\n",
       "      <td>54.1605</td>\n",
       "      <td>6.246276</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3501.354108</td>\n",
       "      <td>83.70</td>\n",
       "      <td>8.10</td>\n",
       "      <td>20.64</td>\n",
       "      <td>27636825.0</td>\n",
       "      <td>153512</td>\n",
       "      <td>270172</td>\n",
       "      <td>2057515.8</td>\n",
       "      <td>684.3906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2</td>\n",
       "      <td>244.0</td>\n",
       "      <td>34.857143</td>\n",
       "      <td>62.0</td>\n",
       "      <td>49</td>\n",
       "      <td>42735725.5</td>\n",
       "      <td>54.4163</td>\n",
       "      <td>4.343692</td>\n",
       "      <td>1785384.00</td>\n",
       "      <td>1026.970279</td>\n",
       "      <td>175.12</td>\n",
       "      <td>22.00</td>\n",
       "      <td>48.24</td>\n",
       "      <td>29644773.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1568244.7</td>\n",
       "      <td>876.2235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>23</td>\n",
       "      <td>11074190.1</td>\n",
       "      <td>58.5923</td>\n",
       "      <td>5.590480</td>\n",
       "      <td>0.00</td>\n",
       "      <td>431.466070</td>\n",
       "      <td>374.62</td>\n",
       "      <td>101.40</td>\n",
       "      <td>264.98</td>\n",
       "      <td>39488891.3</td>\n",
       "      <td>2500</td>\n",
       "      <td>674</td>\n",
       "      <td>1815283.5</td>\n",
       "      <td>506.0778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2</td>\n",
       "      <td>215.0</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>146.0</td>\n",
       "      <td>17</td>\n",
       "      <td>3479195.1</td>\n",
       "      <td>20.3482</td>\n",
       "      <td>29.432858</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2649.372108</td>\n",
       "      <td>54.80</td>\n",
       "      <td>2.79</td>\n",
       "      <td>45.34</td>\n",
       "      <td>6402197.0</td>\n",
       "      <td>33904</td>\n",
       "      <td>2</td>\n",
       "      <td>200477.0</td>\n",
       "      <td>35.7992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4</td>\n",
       "      <td>380.0</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>114.0</td>\n",
       "      <td>22</td>\n",
       "      <td>17688119.8</td>\n",
       "      <td>44.2599</td>\n",
       "      <td>10.640821</td>\n",
       "      <td>5.68</td>\n",
       "      <td>933.761260</td>\n",
       "      <td>129.74</td>\n",
       "      <td>13.29</td>\n",
       "      <td>187.37</td>\n",
       "      <td>17683464.9</td>\n",
       "      <td>191662</td>\n",
       "      <td>144998</td>\n",
       "      <td>3621052.5</td>\n",
       "      <td>258.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>645.0</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>197.0</td>\n",
       "      <td>60</td>\n",
       "      <td>19219569.1</td>\n",
       "      <td>51.0423</td>\n",
       "      <td>9.391091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1377.714893</td>\n",
       "      <td>131.47</td>\n",
       "      <td>11.86</td>\n",
       "      <td>76.26</td>\n",
       "      <td>22902937.4</td>\n",
       "      <td>162151</td>\n",
       "      <td>278500</td>\n",
       "      <td>2079020.2</td>\n",
       "      <td>276.2328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>8</td>\n",
       "      <td>801.0</td>\n",
       "      <td>72.818182</td>\n",
       "      <td>120.0</td>\n",
       "      <td>48</td>\n",
       "      <td>1747653.4</td>\n",
       "      <td>20.0316</td>\n",
       "      <td>34.233040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9863.722082</td>\n",
       "      <td>26.26</td>\n",
       "      <td>3.83</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5863557.4</td>\n",
       "      <td>7638</td>\n",
       "      <td>30050</td>\n",
       "      <td>16865.7</td>\n",
       "      <td>33.5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1</td>\n",
       "      <td>152.0</td>\n",
       "      <td>50.666667</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27</td>\n",
       "      <td>2426143.3</td>\n",
       "      <td>15.0479</td>\n",
       "      <td>24.233323</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4344.419816</td>\n",
       "      <td>51.12</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4648277.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117328.8</td>\n",
       "      <td>40.2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>3</td>\n",
       "      <td>576.1</td>\n",
       "      <td>115.220000</td>\n",
       "      <td>193.5</td>\n",
       "      <td>17</td>\n",
       "      <td>1687448.7</td>\n",
       "      <td>13.2685</td>\n",
       "      <td>20.185969</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3727.475279</td>\n",
       "      <td>40.61</td>\n",
       "      <td>2.94</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3895798.9</td>\n",
       "      <td>11814</td>\n",
       "      <td>49200</td>\n",
       "      <td>25225.0</td>\n",
       "      <td>45.5297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     h1     h2          h3     h4  h5          e1       e2         e3  \\\n",
       "163   1  116.0   58.000000   75.0   9   3558634.5  19.8763  41.822830   \n",
       "65    3  500.0   45.454545   84.0  61  26068595.4  54.1605   6.246276   \n",
       "112   2  244.0   34.857143   62.0  49  42735725.5  54.4163   4.343692   \n",
       "186   1  127.0   63.500000   92.0  23  11074190.1  58.5923   5.590480   \n",
       "155   2  215.0  107.500000  146.0  17   3479195.1  20.3482  29.432858   \n",
       "..   ..    ...         ...    ...  ..         ...      ...        ...   \n",
       "106   4  380.0   95.000000  114.0  22  17688119.8  44.2599  10.640821   \n",
       "14    3  645.0   53.750000  197.0  60  19219569.1  51.0423   9.391091   \n",
       "92    8  801.0   72.818182  120.0  48   1747653.4  20.0316  34.233040   \n",
       "179   1  152.0   50.666667   70.0  27   2426143.3  15.0479  24.233323   \n",
       "102   3  576.1  115.220000  193.5  17   1687448.7  13.2685  20.185969   \n",
       "\n",
       "             e4           v1      v2      v3      v4          v5      c1  \\\n",
       "163        0.00  6365.958035   51.04    6.49    7.98   6336136.7       0   \n",
       "65         0.00  3501.354108   83.70    8.10   20.64  27636825.0  153512   \n",
       "112  1785384.00  1026.970279  175.12   22.00   48.24  29644773.8       0   \n",
       "186        0.00   431.466070  374.62  101.40  264.98  39488891.3    2500   \n",
       "155        0.00  2649.372108   54.80    2.79   45.34   6402197.0   33904   \n",
       "..          ...          ...     ...     ...     ...         ...     ...   \n",
       "106        5.68   933.761260  129.74   13.29  187.37  17683464.9  191662   \n",
       "14         0.00  1377.714893  131.47   11.86   76.26  22902937.4  162151   \n",
       "92         0.00  9863.722082   26.26    3.83    5.60   5863557.4    7638   \n",
       "179        0.00  4344.419816   51.12    7.34    0.00   4648277.3       0   \n",
       "102        0.00  3727.475279   40.61    2.94    4.31   3895798.9   11814   \n",
       "\n",
       "         c2         c3        c4  \n",
       "163       0   213930.2   16.6463  \n",
       "65   270172  2057515.8  684.3906  \n",
       "112       0  1568244.7  876.2235  \n",
       "186     674  1815283.5  506.0778  \n",
       "155       2   200477.0   35.7992  \n",
       "..      ...        ...       ...  \n",
       "106  144998  3621052.5  258.0776  \n",
       "14   278500  2079020.2  276.2328  \n",
       "92    30050    16865.7   33.5460  \n",
       "179       0   117328.8   40.2355  \n",
       "102   49200    25225.0   45.5297  \n",
       "\n",
       "[154 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b2b0754-5ffb-421a-bdf1-cc41acef5d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1</th>\n",
       "      <th>h2</th>\n",
       "      <th>h3</th>\n",
       "      <th>h4</th>\n",
       "      <th>h5</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "      <th>e3</th>\n",
       "      <th>e4</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>v4</th>\n",
       "      <th>v5</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>65.250000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.7808</td>\n",
       "      <td>66.889595</td>\n",
       "      <td>1.020000e+01</td>\n",
       "      <td>8718.377568</td>\n",
       "      <td>36.29</td>\n",
       "      <td>6.90</td>\n",
       "      <td>58.93</td>\n",
       "      <td>1136624.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1481.8</td>\n",
       "      <td>0.7268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>20906084.6</td>\n",
       "      <td>44.6926</td>\n",
       "      <td>9.920600</td>\n",
       "      <td>6.508560e+05</td>\n",
       "      <td>659.477243</td>\n",
       "      <td>121.08</td>\n",
       "      <td>7.39</td>\n",
       "      <td>66.76</td>\n",
       "      <td>13120101.4</td>\n",
       "      <td>22262</td>\n",
       "      <td>31268</td>\n",
       "      <td>701873.0</td>\n",
       "      <td>332.8728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>9</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>57.052632</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>72</td>\n",
       "      <td>1024614.0</td>\n",
       "      <td>13.8948</td>\n",
       "      <td>36.040869</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8487.949766</td>\n",
       "      <td>28.56</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1.35</td>\n",
       "      <td>4285411.8</td>\n",
       "      <td>114162</td>\n",
       "      <td>105055</td>\n",
       "      <td>196037.7</td>\n",
       "      <td>17.4576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>15459473.1</td>\n",
       "      <td>30.2673</td>\n",
       "      <td>3.840314</td>\n",
       "      <td>8.649700e+00</td>\n",
       "      <td>543.290277</td>\n",
       "      <td>112.51</td>\n",
       "      <td>13.85</td>\n",
       "      <td>28.68</td>\n",
       "      <td>17778495.7</td>\n",
       "      <td>8259</td>\n",
       "      <td>1600</td>\n",
       "      <td>1252922.9</td>\n",
       "      <td>650.0555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>38.875000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>12444097.6</td>\n",
       "      <td>23.7534</td>\n",
       "      <td>4.069269</td>\n",
       "      <td>3.202000e+00</td>\n",
       "      <td>801.912729</td>\n",
       "      <td>205.54</td>\n",
       "      <td>22.07</td>\n",
       "      <td>243.14</td>\n",
       "      <td>15491353.3</td>\n",
       "      <td>38082</td>\n",
       "      <td>45707</td>\n",
       "      <td>1845966.3</td>\n",
       "      <td>434.4532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>62</td>\n",
       "      <td>28814296.4</td>\n",
       "      <td>82.5978</td>\n",
       "      <td>7.281506</td>\n",
       "      <td>2.092000e+06</td>\n",
       "      <td>1207.630075</td>\n",
       "      <td>89.30</td>\n",
       "      <td>12.27</td>\n",
       "      <td>88.54</td>\n",
       "      <td>31168936.1</td>\n",
       "      <td>296679</td>\n",
       "      <td>407925</td>\n",
       "      <td>655179.8</td>\n",
       "      <td>886.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>13930312.2</td>\n",
       "      <td>44.8763</td>\n",
       "      <td>9.214224</td>\n",
       "      <td>4.051839e+04</td>\n",
       "      <td>1100.786034</td>\n",
       "      <td>83.46</td>\n",
       "      <td>8.46</td>\n",
       "      <td>42.73</td>\n",
       "      <td>15323043.4</td>\n",
       "      <td>344885</td>\n",
       "      <td>34474</td>\n",
       "      <td>822464.3</td>\n",
       "      <td>391.8971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>60</td>\n",
       "      <td>35651242.2</td>\n",
       "      <td>44.5900</td>\n",
       "      <td>5.085324</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1578.687186</td>\n",
       "      <td>40.48</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1.06</td>\n",
       "      <td>19809122.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>575580.1</td>\n",
       "      <td>712.2340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2</td>\n",
       "      <td>249.100000</td>\n",
       "      <td>49.820000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>36</td>\n",
       "      <td>1608426.6</td>\n",
       "      <td>37.8839</td>\n",
       "      <td>7.061085</td>\n",
       "      <td>6.957990e+05</td>\n",
       "      <td>1077.962449</td>\n",
       "      <td>119.83</td>\n",
       "      <td>43.13</td>\n",
       "      <td>73.75</td>\n",
       "      <td>17029972.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1716186.2</td>\n",
       "      <td>249.2257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0</td>\n",
       "      <td>218.513191</td>\n",
       "      <td>36.418865</td>\n",
       "      <td>56.838394</td>\n",
       "      <td>32</td>\n",
       "      <td>18895390.6</td>\n",
       "      <td>61.0211</td>\n",
       "      <td>8.058305</td>\n",
       "      <td>3.470160e+05</td>\n",
       "      <td>450.374792</td>\n",
       "      <td>97.46</td>\n",
       "      <td>7.48</td>\n",
       "      <td>158.90</td>\n",
       "      <td>25494244.4</td>\n",
       "      <td>195935</td>\n",
       "      <td>298637</td>\n",
       "      <td>905825.5</td>\n",
       "      <td>571.3138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     h1           h2         h3          h4  h5          e1       e2  \\\n",
       "132   2   261.000000  65.250000  107.000000  26        53.0   4.7808   \n",
       "148   1   170.000000  42.500000   68.000000  15  20906084.6  44.6926   \n",
       "93    9  1084.000000  57.052632  108.000000  72   1024614.0  13.8948   \n",
       "180   1   256.000000  64.000000  116.000000  28  15459473.1  30.2673   \n",
       "15    2   311.000000  38.875000   67.000000  50  12444097.6  23.7534   \n",
       "..   ..          ...        ...         ...  ..         ...      ...   \n",
       "5     0   297.000000  27.000000   58.000000  62  28814296.4  82.5978   \n",
       "139   3   318.000000  53.000000   87.000000  26  13930312.2  44.8763   \n",
       "56    7   750.000000  62.500000  163.000000  60  35651242.2  44.5900   \n",
       "156   2   249.100000  49.820000   74.500000  36   1608426.6  37.8839   \n",
       "176   0   218.513191  36.418865   56.838394  32  18895390.6  61.0211   \n",
       "\n",
       "            e3            e4           v1      v2     v3      v4          v5  \\\n",
       "132  66.889595  1.020000e+01  8718.377568   36.29   6.90   58.93   1136624.0   \n",
       "148   9.920600  6.508560e+05   659.477243  121.08   7.39   66.76  13120101.4   \n",
       "93   36.040869  0.000000e+00  8487.949766   28.56   4.64    1.35   4285411.8   \n",
       "180   3.840314  8.649700e+00   543.290277  112.51  13.85   28.68  17778495.7   \n",
       "15    4.069269  3.202000e+00   801.912729  205.54  22.07  243.14  15491353.3   \n",
       "..         ...           ...          ...     ...    ...     ...         ...   \n",
       "5     7.281506  2.092000e+06  1207.630075   89.30  12.27   88.54  31168936.1   \n",
       "139   9.214224  4.051839e+04  1100.786034   83.46   8.46   42.73  15323043.4   \n",
       "56    5.085324  0.000000e+00  1578.687186   40.48   3.95    1.06  19809122.5   \n",
       "156   7.061085  6.957990e+05  1077.962449  119.83  43.13   73.75  17029972.4   \n",
       "176   8.058305  3.470160e+05   450.374792   97.46   7.48  158.90  25494244.4   \n",
       "\n",
       "         c1      c2         c3        c4  \n",
       "132       0       0     1481.8    0.7268  \n",
       "148   22262   31268   701873.0  332.8728  \n",
       "93   114162  105055   196037.7   17.4576  \n",
       "180    8259    1600  1252922.9  650.0555  \n",
       "15    38082   45707  1845966.3  434.4532  \n",
       "..      ...     ...        ...       ...  \n",
       "5    296679  407925   655179.8  886.0230  \n",
       "139  344885   34474   822464.3  391.8971  \n",
       "56        0       0   575580.1  712.2340  \n",
       "156       0       0  1716186.2  249.2257  \n",
       "176  195935  298637   905825.5  571.3138  \n",
       "\n",
       "[66 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07eb4df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 15143829109922.113\n",
      "R2 : 0.8413961989041499\n",
      "R2 : 0.6995485557276906\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(max_depth=100 ,max_features='log2', min_samples_leaf=1, min_samples_split=3,  n_estimators=35, random_state=42)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = rf.predict(X_train)\n",
    "y_test_pred = rf.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_test_pred)\n",
    "print('MSE:', mse)\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"R2 : \" + str(r2_score(y_train, y_train_pred)))\n",
    "print(\"R2 : \" + str(r2_score(y_test, y_test_pred)))\n",
    "\n",
    "#import hydroeval as he\n",
    "#nse = he.evaluator(he.nse, y_test_pred, y_test)\n",
    "#kge, r, alpha, beta = he.evaluator(he.kge, y_test_pred, y_test)\n",
    "\n",
    "#print(nse, kge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "debf1731",
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_result_tr = pd.DataFrame(y_train,columns = ['Actual_tr'])\n",
    "flood_result_tr['Actual_tr'] = y_train\n",
    "flood_result_tr['Predicted_tr'] = y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b8f38c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_result_tr.to_csv('train_flood_rfmodel.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e9064b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_result_te = pd.DataFrame(y_test,columns = ['Actual_te'])\n",
    "flood_result_te['Actual_te'] = y_test\n",
    "flood_result_te['Predicted_te'] = y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73082049",
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_result_te.to_csv('test_flood_rfmodel.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092eaf9a-8eb2-4f43-a450-5e92331362dd",
   "metadata": {},
   "source": [
    "# Fig. 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b93888db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163</td>\n",
       "      <td>2.363704e+03</td>\n",
       "      <td>1.203276e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>1.459136e+06</td>\n",
       "      <td>4.154886e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>112</td>\n",
       "      <td>9.487370e+05</td>\n",
       "      <td>3.870754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>186</td>\n",
       "      <td>2.508122e+05</td>\n",
       "      <td>6.423163e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>6.588848e+05</td>\n",
       "      <td>1.470201e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Actual     Predicted\n",
       "0         163  2.363704e+03  1.203276e+05\n",
       "1          65  1.459136e+06  4.154886e+06\n",
       "2         112  9.487370e+05  3.870754e+06\n",
       "3         186  2.508122e+05  6.423163e+05\n",
       "4         155  6.588848e+05  1.470201e+06"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flood_result = pd.read_csv(\"flood_result_rf.csv\", encoding= 'cp949')\n",
    "flood_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91db10dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 14))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "B=pd.DataFrame(flood_result[:154])\n",
    "C=pd.DataFrame(flood_result[154:])\n",
    "\n",
    "x=flood_result.Actual\n",
    "plt.plot(x, x, color='#7f7f7f', linewidth=1)\n",
    "\n",
    "ax.scatter(B.Actual, B.Predicted, label='Train Period (NSE: 0.84)', color='b', s=100, linewidth=3)\n",
    "ax.scatter(C.Actual, C.Predicted, label='Test Period (NSE: 0.7)', marker='o', s=100, linewidth=3, facecolors='none', edgecolors='r')\n",
    "\n",
    "ax.set_xlabel('Actual economic loss', fontsize=50)\n",
    "ax.set_ylabel('Predicted economic loss', fontsize=50)\n",
    "plt.xticks(fontsize=45)\n",
    "plt.yticks(fontsize=45)\n",
    "ax.legend(fontsize=35, loc='best')\n",
    "\n",
    "plt.savefig('Flood_RF model result.png', dpi=300)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1041effb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.69954856] [0.54089169]\n"
     ]
    }
   ],
   "source": [
    "import hydroeval as he\n",
    "nse = he.evaluator(he.nse, y_test_pred, y_test)\n",
    "kge, r, alpha, beta = he.evaluator(he.kge, y_test_pred, y_test)\n",
    "\n",
    "print(nse, kge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10556462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h1: 0.03554669223092588\n",
      "h2: 0.09117306462178079\n",
      "h3: 0.051537314315566816\n",
      "h4: 0.1863834044286477\n",
      "h5: 0.12771324708569542\n",
      "e1: 0.07509236026440194\n",
      "e2: 0.027131831467646427\n",
      "e3: 0.058764447543982135\n",
      "e4: 0.0226734189768004\n",
      "v1: 0.037458624547099036\n",
      "v2: 0.025853425962213124\n",
      "v3: 0.046815038276149454\n",
      "v4: 0.035297928132514575\n",
      "v5: 0.029829517023856778\n",
      "c1: 0.02792407404361954\n",
      "c2: 0.01617729506712987\n",
      "c3: 0.06502230835735849\n",
      "c4: 0.03960600765461179\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "feature_importances = rf.feature_importances_\n",
    "\n",
    "for feature_name, importance in zip(X.columns, feature_importances):\n",
    "    print(f'{feature_name}: {importance}')\n",
    "\n",
    "sorted_indices = feature_importances.argsort()[::-1]\n",
    "sorted_importances = feature_importances[sorted_indices]\n",
    "sorted_features = X.columns[sorted_indices]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(feature_importances)), sorted_importances, align='center', width=0.5)  # 막대 굵기 조절\n",
    "plt.xticks(range(len(feature_importances)), sorted_features, fontsize=19)  # x축 눈금 크기와 레이블 크기 조절\n",
    "plt.yticks(fontsize=15)  # y축 눈금 크기 조절\n",
    "plt.xlabel('Features', fontsize=25)  # x축 레이블 크기 조절\n",
    "plt.ylabel('Importance', fontsize=25)  # y축 레이블 크기 조절\n",
    "plt.title('Feature Importance', fontsize=23)  # 타이틀 크기 조절\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14288f6d-b876-4b5c-b12d-fd809d24ac93",
   "metadata": {},
   "source": [
    "# Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fe98deca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 15143829109922.113\n",
      "R2 (Train): 0.8413961989041499\n",
      "R2 (Test): 0.6995485557276906\n",
      "Permutation Importance of h1: 0.08864418787840646, p-value: 0.001646787415850115\n",
      "Permutation Importance of h2: 0.1285543711990831, p-value: 0.001075500153218778\n",
      "Permutation Importance of h3: 0.031080744664433887, p-value: 0.07171263303603692\n",
      "Permutation Importance of h4: 0.3768556148605047, p-value: 2.091940805248882e-06\n",
      "Permutation Importance of h5: 0.013412711545263637, p-value: 0.44963326560436645\n",
      "Permutation Importance of e1: 0.1570333568327999, p-value: 3.4060364377141994e-05\n",
      "Permutation Importance of e2: 0.023661132700284727, p-value: 0.04010062529693936\n",
      "Permutation Importance of e3: 0.08212552985227595, p-value: 0.043166338686674965\n",
      "Permutation Importance of e4: 0.05620673299690796, p-value: 0.011972289916569068\n",
      "Permutation Importance of v1: 0.04098611415752779, p-value: 0.08140682256065213\n",
      "Permutation Importance of v2: 0.025555210037566823, p-value: 0.12975658424562608\n",
      "Permutation Importance of v3: 0.0773953232511603, p-value: 0.0008372198204169568\n",
      "Permutation Importance of v4: 0.03952126093707448, p-value: 0.012146365714376994\n",
      "Permutation Importance of v5: 0.030381833591498604, p-value: 0.007433216351274696\n",
      "Permutation Importance of c1: 0.003541762520672508, p-value: 0.7621791258994124\n",
      "Permutation Importance of c2: 0.019045067266112638, p-value: 0.0054242924632041944\n",
      "Permutation Importance of c3: 0.026312256638852432, p-value: 0.2338978159210967\n",
      "Permutation Importance of c4: 0.12310094341271317, p-value: 0.00029723668603165443\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.inspection import permutation_importance\n",
    "from scipy import stats\n",
    "\n",
    "X = df[['h1', 'h2', 'h3', 'h4', 'h5', 'e1', 'e2', 'e3', 'e4', 'v1', 'v2', 'v3', 'v4', 'v5', 'c1', 'c2', 'c3', 'c4']]\n",
    "y = df['damage']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "rf = RandomForestRegressor(max_depth=100 ,max_features='log2', min_samples_leaf=1, min_samples_split=3,  n_estimators=35, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = rf.predict(X_train)\n",
    "y_test_pred = rf.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_test_pred)\n",
    "print('MSE:', mse)\n",
    "print(\"R2 (Train): \" + str(r2_score(y_train, y_train_pred)))\n",
    "print(\"R2 (Test): \" + str(r2_score(y_test, y_test_pred)))\n",
    "\n",
    "perm_importance = permutation_importance(rf, X_test, y_test, n_repeats=1000, random_state=42)\n",
    "\n",
    "for i, feature in enumerate(X.columns):\n",
    "    perm_importance_mean = perm_importance.importances_mean[i]\n",
    "    perm_importance_std = perm_importance.importances_std[i]\n",
    "    z_score = perm_importance_mean / perm_importance_std  \n",
    "    p_value = 2 * (1 - stats.norm.cdf(abs(z_score)))  \n",
    "    print(f'Permutation Importance of {feature}: {perm_importance_mean}, p-value: {p_value}')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "bp = plt.boxplot(perm_importance.importances.T, labels=X.columns, vert=False, showfliers=False, patch_artist=True)\n",
    "\n",
    "for box in bp['medians']:\n",
    "    box.set(color='red', linewidth=2)\n",
    "\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Permutation Importance')\n",
    "plt.title('Permutation Importance for Features')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3227661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "perm_importance_data = perm_importance.importances.T[:, ::-1]\n",
    "labels = X.columns[::-1]\n",
    "\n",
    "bp = plt.boxplot(perm_importance_data, labels=labels, vert=False, showfliers=False, patch_artist=True)\n",
    "\n",
    "for box in bp['medians']:\n",
    "    box.set(color='black', linewidth=3)\n",
    "\n",
    "for box in bp['boxes']:\n",
    "    box.set(facecolor='white')  \n",
    "plt.xlabel('Permutation Importance', fontsize=33)\n",
    "plt.ylabel('AHP Index', fontsize=35)\n",
    "plt.xticks(fontsize=23)\n",
    "plt.yticks(fontsize=22)\n",
    "\n",
    "plt.savefig('Permutation Importance.png', dpi=800, bbox_inches='tight')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "88c8615f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "importance_values = perm_importance.importances\n",
    "\n",
    "confidence_intervals = []\n",
    "for vals in importance_values:\n",
    "    lower_bound = round(np.percentile(vals, 2.5), 4)  # 2.5% 백분위수\n",
    "    upper_bound = round(np.percentile(vals, 97.5), 4)  # 97.5% 백분위수\n",
    "    confidence_intervals.append((lower_bound, upper_bound))\n",
    "\n",
    "std_deviation = [round(np.std(vals), 3) for vals in importance_values] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0f0ed0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0311, 0.1407),\n",
       " (0.0441, 0.1968),\n",
       " (-0.0018, 0.0664),\n",
       " (0.1924, 0.5078),\n",
       " (-0.0258, 0.0456),\n",
       " (0.0765, 0.2312),\n",
       " (0.0042, 0.048),\n",
       " (0.0057, 0.1613),\n",
       " (0.0136, 0.0996),\n",
       " (0.0013, 0.0882),\n",
       " (-0.0027, 0.0635),\n",
       " (0.033, 0.1247),\n",
       " (0.0126, 0.0733),\n",
       " (0.0074, 0.0512),\n",
       " (-0.0177, 0.027),\n",
       " (0.0054, 0.0323),\n",
       " (-0.0156, 0.0688),\n",
       " (0.0547, 0.1847)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "89925aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature    MIN  Median    MAX   Mean 95% Confidence Interval  \\\n",
      "0       h1  0.012   0.089  0.167  0.089        (0.0311, 0.1407)   \n",
      "1       h2 -0.005   0.132  0.232  0.129        (0.0441, 0.1968)   \n",
      "2       h3 -0.016   0.030  0.102  0.031       (-0.0018, 0.0664)   \n",
      "3       h4  0.044   0.389  0.562  0.377        (0.1924, 0.5078)   \n",
      "4       h5 -0.051   0.014  0.064  0.013       (-0.0258, 0.0456)   \n",
      "5       e1 -0.001   0.158  0.290  0.157        (0.0765, 0.2312)   \n",
      "6       e2 -0.006   0.023  0.066  0.024         (0.0042, 0.048)   \n",
      "7       e3 -0.021   0.083  0.198  0.082        (0.0057, 0.1613)   \n",
      "8       e4 -0.008   0.056  0.118  0.056        (0.0136, 0.0996)   \n",
      "9       v1 -0.015   0.039  0.112  0.041        (0.0013, 0.0882)   \n",
      "10      v2 -0.017   0.024  0.081  0.026       (-0.0027, 0.0635)   \n",
      "11      v3  0.010   0.077  0.152  0.077         (0.033, 0.1247)   \n",
      "12      v4  0.006   0.039  0.089  0.040        (0.0126, 0.0733)   \n",
      "13      v5 -0.006   0.031  0.067  0.030        (0.0074, 0.0512)   \n",
      "14      c1 -0.026   0.003  0.043  0.004        (-0.0177, 0.027)   \n",
      "15      c2 -0.002   0.019  0.040  0.019        (0.0054, 0.0323)   \n",
      "16      c3 -0.043   0.026  0.094  0.026       (-0.0156, 0.0688)   \n",
      "17      c4  0.019   0.124  0.221  0.123        (0.0547, 0.1847)   \n",
      "\n",
      "    Standard Deviation  p-value  \n",
      "0                0.028  0.00165  \n",
      "1                0.039  0.00108  \n",
      "2                0.017  0.07171  \n",
      "3                0.079  0.00000  \n",
      "4                0.018  0.44963  \n",
      "5                0.038  0.00003  \n",
      "6                0.012  0.04010  \n",
      "7                0.041  0.04317  \n",
      "8                0.022  0.01197  \n",
      "9                0.024  0.08141  \n",
      "10               0.017  0.12976  \n",
      "11               0.023  0.00084  \n",
      "12               0.016  0.01215  \n",
      "13               0.011  0.00743  \n",
      "14               0.012  0.76218  \n",
      "15               0.007  0.00542  \n",
      "16               0.022  0.23390  \n",
      "17               0.034  0.00030  \n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame({'Feature': X.columns,\n",
    "                           'Min': [round(min(vals), 3) for vals in importance_values],\n",
    "                           'Median': [round(np.median(vals), 3) for vals in importance_values],\n",
    "                           'Max': [round(max(vals), 3) for vals in importance_values],\n",
    "                           'Mean': [round(np.mean(vals), 3) for vals in importance_values],\n",
    "                           '95% Confidence Interval': confidence_intervals,\n",
    "                           'Standard Deviation': std_deviation})\n",
    "p_values = []\n",
    "for i, feature in enumerate(X.columns):\n",
    "    perm_importance_mean = perm_importance.importances_mean[i]\n",
    "    perm_importance_std = perm_importance.importances_std[i]\n",
    "    z_score = perm_importance_mean / perm_importance_std  # Z-점수 계산\n",
    "    p_value = 2 * (1 - stats.norm.cdf(abs(z_score)))  # 양측 검정을 위한 p-value 계산\n",
    "    p_values.append(round(p_value, 5))\n",
    "\n",
    "results_df['p-value'] = p_values \n",
    "\n",
    "print(results_df)\n",
    "\n",
    "#results_df.to_excel('permutation_importance_95%.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10a2f79-5abb-41ca-a6ed-9650360ca815",
   "metadata": {},
   "source": [
    "# Fig4. (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4d7832c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H1</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index     min     max      w\n",
       "0    H1  0.0311  0.1407  0.036"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"95%.csv\", encoding= 'cp949')\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "551c6d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 에러바 차트 그리기\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "x = df['index']\n",
    "y = (df['min'] + df['max']) / 2\n",
    "yerr = (df['max'] - df['min']) / 2\n",
    "plt.axhline(y=0, color='black', linewidth=1) \n",
    "# 색상을 지정할 구간 정의\n",
    "color_indices = [0, 5, 9, 14]\n",
    "\n",
    "for i in range(len(x)):\n",
    "    plt.errorbar(x[i], y[i], yerr=yerr[i], fmt='o', markersize=12, capsize=7, label='Error Bar', color='red' if i in range(color_indices[0], color_indices[1]) else 'orange' if i in range(color_indices[1], color_indices[2]) else 'blueviolet' if i in range(color_indices[2], color_indices[3]) else 'dodgerblue', linewidth=3)\n",
    "\n",
    "plt.xticks(fontsize=21)\n",
    "plt.ylabel('95% Confidence Interval', fontsize=30)\n",
    "plt.yticks(fontsize=25)\n",
    "#plt.title('Error Bar Chart')\n",
    "#plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.savefig('95% Permutation Importance.png', dpi=1000, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58763a6d-66f0-4e01-805a-083bb74aa59b",
   "metadata": {},
   "source": [
    "# Fig. 4 (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12f98d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub</th>\n",
       "      <th>value</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H1</td>\n",
       "      <td>0.089</td>\n",
       "      <td>Hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H2</td>\n",
       "      <td>0.129</td>\n",
       "      <td>Hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H3</td>\n",
       "      <td>0.031</td>\n",
       "      <td>Hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H4</td>\n",
       "      <td>0.377</td>\n",
       "      <td>Hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H5</td>\n",
       "      <td>0.013</td>\n",
       "      <td>Hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E1</td>\n",
       "      <td>0.157</td>\n",
       "      <td>Exposure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E2</td>\n",
       "      <td>0.024</td>\n",
       "      <td>Exposure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E3</td>\n",
       "      <td>0.082</td>\n",
       "      <td>Exposure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E4</td>\n",
       "      <td>0.056</td>\n",
       "      <td>Exposure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>V1</td>\n",
       "      <td>0.041</td>\n",
       "      <td>Vulnerability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>V2</td>\n",
       "      <td>0.026</td>\n",
       "      <td>Vulnerability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>V3</td>\n",
       "      <td>0.077</td>\n",
       "      <td>Vulnerability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>V4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>Vulnerability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>V5</td>\n",
       "      <td>0.030</td>\n",
       "      <td>Vulnerability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.004</td>\n",
       "      <td>Capacity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C2</td>\n",
       "      <td>0.019</td>\n",
       "      <td>Capacity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C3</td>\n",
       "      <td>0.026</td>\n",
       "      <td>Capacity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C4</td>\n",
       "      <td>0.123</td>\n",
       "      <td>Capacity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  value          index\n",
       "0   H1  0.089         Hazard\n",
       "1   H2  0.129         Hazard\n",
       "2   H3  0.031         Hazard\n",
       "3   H4  0.377         Hazard\n",
       "4   H5  0.013         Hazard\n",
       "5   E1  0.157       Exposure\n",
       "6   E2  0.024       Exposure\n",
       "7   E3  0.082       Exposure\n",
       "8   E4  0.056       Exposure\n",
       "9   V1  0.041  Vulnerability\n",
       "10  V2  0.026  Vulnerability\n",
       "11  V3  0.077  Vulnerability\n",
       "12  V4  0.040  Vulnerability\n",
       "13  V5  0.030  Vulnerability\n",
       "14  C1  0.004       Capacity\n",
       "15  C2  0.019       Capacity\n",
       "16  C3  0.026       Capacity\n",
       "17  C4  0.123       Capacity"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"donut_chart_permutation.csv\", encoding= 'cp949')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "468ed642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcl\n",
    "import matplotlib.patches as mpt\n",
    "import seaborn as sns\n",
    " \n",
    "freq_col = 'value' \n",
    "outer_col = 'index' \n",
    "inner_col = 'sub' \n",
    " \n",
    "size = 0.35 \n",
    "threshold = 1 \n",
    " \n",
    "color = ['dodgerblue', 'orange', 'r', 'blueviolet']  \n",
    " \n",
    "summary = df.groupby(outer_col)[freq_col].sum().reset_index()\n",
    "outer_data = summary[freq_col] \n",
    "inner_data = [] \n",
    "for s in summary[outer_col]:\n",
    "    inner_data += list(df.query('{0}==@s'.format(outer_col))[freq_col])\n",
    " \n",
    "fig = plt.figure(figsize=(15,15)) \n",
    "fig.set_facecolor('white') \n",
    "ax = fig.add_subplot() \n",
    " \n",
    "out_pie = ax.pie(outer_data,\n",
    "             radius=1,\n",
    "             colors=color,\n",
    "             wedgeprops=dict(width=size,edgecolor='w'))\n",
    " \n",
    "total = np.sum(outer_data) \n",
    " \n",
    "sum_pct = 0\n",
    " \n",
    "for i in range(len(outer_data)):\n",
    "    ang1, ang2 = out_pie[0][i].theta1, out_pie[0][i].theta2\n",
    "    out_r = out_pie[0][i].r \n",
    "    \n",
    "    x = ((2*out_r-size)/2)*np.cos(np.pi/180*((ang1+ang2)/2)) \n",
    "    y = ((2*out_r-size)/2)*np.sin(np.pi/180*((ang1+ang2)/2)) \n",
    "    \n",
    "    if i < len(outer_data) - 1:\n",
    "        sum_pct += float(f'{outer_data[i]/total*100:.1f}') \n",
    "        ax.text(x,y,f'{outer_data[i]/total*100:.1f}%',ha='center',va='center', fontsize=23,fontweight='bold')\n",
    "    else: \n",
    "        ax.text(x,y,f'{100-sum_pct:.1f}%',ha='center',va='center', fontsize=23,fontweight='bold')\n",
    " \n",
    "outer_color = [] \n",
    "for p in out_pie[0]:\n",
    "    outer_color.append(p.get_facecolor()) \n",
    "outer_color_hsv = [mcl.rgb_to_hsv(x[:3]) for x in outer_color] \n",
    "outer_color_hsv = [(x[0],x[1],1) for x in outer_color_hsv] \n",
    " \n",
    "inner_color = [] \n",
    "for i, g in enumerate(summary[outer_col]):\n",
    "    num_sub_group = len(df.query('{0}==@g'.format(outer_col))) \n",
    "    jump = outer_color_hsv[i][1]/(num_sub_group+1) \n",
    "    temp_list = []\n",
    "    temp_s = np.arange(0,outer_color_hsv[i][1],jump) \n",
    "    temp_s = temp_s[1:]\n",
    "    for t in temp_s:\n",
    "        h = outer_color_hsv[i][0] \n",
    "        s = t \n",
    "        v = outer_color_hsv[i][2] \n",
    "        temp_list.append((h,s,v))\n",
    "    inner_color += temp_list[::-1] \n",
    "    \n",
    "inner_color = [mcl.hsv_to_rgb(x) for x in inner_color] \n",
    " \n",
    "\n",
    "inner_pie = ax.pie(inner_data,\n",
    "       radius=1-size,\n",
    "       colors=inner_color,\n",
    "       wedgeprops=dict(width=size,edgecolor='w'))\n",
    " \n",
    "bbox_props = dict(boxstyle='square',fc='w',ec='w',alpha=0) \n",
    "config = dict(arrowprops=dict(arrowstyle='-'),bbox=bbox_props,va='center')\n",
    " \n",
    "inner_sum_pct = 0 \n",
    "for i in range(len(inner_data)):\n",
    "    ang1, ang2 = inner_pie[0][i].theta1, inner_pie[0][i].theta2\n",
    "    r = inner_pie[0][i].r \n",
    "    \n",
    "    x = ((2*r-size)/2)*np.cos(np.pi/180*((ang1+ang2)/2)) \n",
    "    y = ((2*r-size)/2)*np.sin(np.pi/180*((ang1+ang2)/2)) \n",
    "    \n",
    "    if i < len(inner_data) - 1:\n",
    "        inner_sum_pct += float(f'{inner_data[i]/total*100:.1f}') \n",
    "        text = f'{inner_data[i]/total*100:.1f}'\n",
    "    else: \n",
    "        text = f'{100-inner_sum_pct:.1f}' \n",
    "\n",
    "    \n",
    "    if inner_data[i]/total*100 < threshold:\n",
    "        ang = (ang1+ang2)/2 ## 중심각\n",
    "        x = out_r*np.cos(np.deg2rad(ang)) \n",
    "        y = out_r*np.sin(np.deg2rad(ang)) \n",
    "   \n",
    "        horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
    "        connectionstyle = \"angle,angleA=0,angleB={}\".format(ang) \n",
    "        config[\"arrowprops\"].update({\"connectionstyle\": connectionstyle}) \n",
    "        ax.annotate(text, xy=((out_r-size)*x, (out_r-size)*y), xytext=(1.5*x, 1.2*y),\n",
    "                    horizontalalignment=horizontalalignment, **config, fontsize=22)\n",
    "    else:\n",
    "      \n",
    "        x = ((2*r-size)/2)*np.cos(np.pi/180*((ang1+ang2)/2)) \n",
    "        y = ((2*r-size)/2)*np.sin(np.pi/180*((ang1+ang2)/2)) \n",
    "        ax.text(x, y, text, ha='center', va='center', fontsize=22)\n",
    " \n",
    "\n",
    "inner_pie_index = -1 \n",
    "right_legend_patches = [] \n",
    "left_legend_patches = [] \n",
    "right_labels = [] \n",
    "left_labels = []\n",
    "for i in range(len(outer_data)):\n",
    "    left_legend_patches.append(out_pie[0][i])\n",
    "    \n",
    "    outer_label = summary[outer_col][i] \n",
    " \n",
    "    left_labels.append(outer_label)\n",
    "    temp_data = df.query('{0}==@outer_label'.format(outer_col)) \n",
    "    temp_data = temp_data.reset_index(drop=True)\n",
    "    \n",
    "    temp_number = len(temp_data)-1\n",
    "    \n",
    "    for k in range(temp_number):\n",
    "        rect = mpt.Rectangle((0,0),1,1.1,facecolor='None')\n",
    "        left_legend_patches.append(rect)\n",
    "        left_labels.append('')\n",
    "\n",
    "    for j in range(len(temp_data)):\n",
    "        inner_pie_index += 1\n",
    "        \n",
    "        right_legend_patches.append(inner_pie[0][inner_pie_index])\n",
    "        right_labels.append(temp_data[inner_col][j])\n",
    "        \n",
    "   \n",
    "    legend_patches = left_legend_patches+right_legend_patches\n",
    "    labels = left_labels + right_labels\n",
    "        \n",
    "\n",
    "plt.legend(legend_patches,\n",
    "           labels,\n",
    "           ncol=2,\n",
    "           #loc='upper left',\n",
    "           handleheight=1, \n",
    "           labelspacing=0.5, \n",
    "           bbox_to_anchor=(0.1,1), fontsize=17)\n",
    "\n",
    "plt.savefig('Dounut Chart_AHP_Permutation.png', dpi=800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53a250be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
